Generating a platform-based representative image for a digital video

ABSTRACT

The present disclosure relates to systems, methods, and computer-readable media that generate a platform-specific representative image (e.g., a thumbnail image) for a digital video that is predicted to effectively engage users of a communication system (e.g., a social media system). For example, systems described herein include identifying keywords and associated engagement scores for the content sharing platform. The systems described herein further include identifying keywords associated with segments of the digital video. The systems can further determine a segment of interest based on the engagement scores for the content sharing platform and a semantic relationship between keywords for the content sharing platform and one or more keywords associated respective segments of the digital video. The system can further determine a representative image that effectively engages users of the sharing platform by determining a representative image from the identified segment of interest.

BACKGROUND

Recent years have seen a rise in the use of computing devices (e.g.,mobile devices, personal computers) to capture, store, and share digitalmedia over a variety of platforms. Indeed, it is now common forindividuals to use computing devices to share digital videos and imagesacross a variety of communication platforms (e.g., social networkingplatforms and other digital communication platforms). In providingaccess to shared digital videos, many conventional systems display athumbnail image representative of a corresponding digital video. Forexample, rather than streaming or otherwise playing back an entire videoautomatically upon opening a webpage or communication application,conventional systems often generate a thumbnail image that provides apreview of the digital video and playback the digital video only upondetecting a user selection of the thumbnail image. Conventional methodsfor displaying a thumbnail image representative of a shared digitalvideo, however, suffer from a number of problems and inefficiencies.

For example, conventional systems for generating a thumbnail image for acorresponding digital video typically fail to accurately determine oridentify a frame from the digital video that will effectively engagerecipients of the digital video. For instance, many conventional systemssimply identify a random video frame to use as a thumbnail image,resulting in a thumbnail image that fails to effectively engageprospective viewers of the digital video. Alternatively, someconventional systems analyze a digital video to identify a high-qualityvideo frame. While identifying a high-quality video frame is generallymore effective at engaging viewers than identifying a random videoframe, simply identifying a high-quality video frame to use for athumbnail image often fails to capture attention or engage a particularaudience.

In addition, conventional systems often consume significant processingresources to non-randomly generate or otherwise identify a thumbnailimage. For example, identifying a high-quality video frame ofteninvolves analyzing a large number of video frames across the duration ofthe digital video resulting in a computationally prohibitive process.Thus, conventional systems for generating thumbnail images generallyconsume significant processing resources thereby increasing processingcosts and reducing effectiveness of computing devices utilized byconventional systems.

These and other problems exist with regard to generating and presentingrepresentative images for a digital video.

SUMMARY

Embodiments of the present disclosure provide benefits and/or solve oneor more of the foregoing and other problems in the art with systems,methods, and non-transitory computer-readable media that generate orotherwise identify representative images for digital videos presentationtailored to a particular communication platform on which the digitalvideos will be provided. In particular, in one or more embodiments, thedisclosed systems observe engagement of users of a communication system(e.g., a social media system) with respect to digital content shared viaa content sharing platform (e.g., a page of the social networkingsystem) to identify keywords associated with various levels ofengagement. In addition, the disclosed systems can identify keywordsassociated with segments of a digital video. The disclosed systems canfurther analyze correlations between the keywords associated with thecontent sharing platform and keywords associated with respectivesegments of the digital video to determine a representative imagepredicted to engage users of the communication system associated withthe particular content sharing platform.

By identifying correlations between keywords of the content sharingplatform and keywords corresponding to respective segments of a digitalvideo, the disclosed systems can more accurately identify arepresentative image for the digital video expected to engage aparticular group of users of the communication system. In addition, byconsidering keywords of the content sharing platform on which thedigital video is shared, the disclosed systems can more flexiblyidentify different representative images for a number of differentcontent sharing platforms.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of such exemplary embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates a block diagram of an environment in which athumbnail generation system is implemented in accordance with one ormore embodiments;

FIG. 2 illustrates an overview of a process of generating arepresentative image for a digital video in accordance with one or moreembodiments;

FIG. 3 illustrates an overview of a process of generating arepresentative image for a digital video in accordance with one or moreembodiments;

FIG. 4 illustrates an example embodiment in which a thumbnail generationsystem determines correlations between keywords of a digital video andkeywords of a content sharing platform in accordance with one or moreembodiments;

FIG. 5 illustrates an example embodiment in which a thumbnail generationsystem generates representative images of a digital video forpresentation via different content sharing platforms in accordance withone or more embodiments;

FIG. 6 illustrates a schematic diagram of an example architecture of athumbnail generation system in accordance with one or more embodiments;

FIG. 7 illustrates a flow diagram of an example series of acts forgenerating a representative image for a digital video in accordance withone or more embodiments; and

FIG. 8 illustrates a block diagram of an example computing device inaccordance with one or more embodiments.

DETAILED DESCRIPTION

One or more embodiments of the present disclosure include a thumbnailgeneration system that generates a representative image from a keysegment of a digital video based on correlations between keywordsassociated with the segment of the digital video and keywords associatedwith a corresponding content sharing platform. For example, uponidentifying a digital video to share with users of a communicationsystem via a content sharing platform of the communication system (e.g.,a group page, profile page), the thumbnail generation system canidentify keywords associated with the content sharing platform. Thethumbnail generation system can further identify keywords associatedwith segments of the digital video to determine (e.g., generate) arepresentative image (e.g., a thumbnail image) from a segment ofinterest of the digital video predicted to effectively engage users ofthe content sharing platform. As will be described in further detailbelow, in this manner, the thumbnail generation system can accurately,efficiently, and flexibly engage users across any number of contentsharing platforms.

To illustrate, in one or more embodiments, the thumbnail generationsystem can receive or otherwise identify a digital video for sharingwith users of a communication system. In particular, the thumbnailgeneration system can receive or identify a digital video to provide toa select group of users of the communication system that have access toa content sharing platform of the communication system (e.g., members ofthe content sharing platform). For example, the thumbnail generationsystem can receive a digital video from a user of the communicationsystem to share with other users of the communication system. As anotherexample, the thumbnail generation system can receive or identify a videoadvertisement to provide to users of the communication system via thecontent sharing platform.

In one or more embodiments, the thumbnail generation system determineskeywords associated with the content sharing platform. For example, inone or more embodiments, the thumbnail generation system analyzesinteractions by a plurality of users with respect to content (e.g.posts, comments, images, videos) shared via the content sharingplatform. The thumbnail generation system can additionally identify oneor more terms (e.g., keywords) associated with the shared content todetermine keywords associated with different levels of engagement by theplurality of users having access to the content sharing platform. Inparticular, as will be described in further detail below, the thumbnailgeneration system can identify a plurality of keywords and determineengagement scores for the identified keywords unique to content sharedvia the content sharing platform.

In addition to determining keywords associated with the content sharingplatform, the thumbnail generation system can additionally identifykeywords associated with the digital video to be provided to users ofthe communication system via the content sharing platform. Inparticular, in one or more embodiments, the thumbnail generation systemdetermines one or more keywords associated with respective segments(e.g., discrete clips) of the digital video. For example, the thumbnailgeneration system can divide the digital video into any number ofdiscrete segments and identify one or more keywords corresponding to therespective segments of the digital video. As will be described infurther detail below, the thumbnail generation system can analyze visualcontent, textual content, and audio content of individual segments ofthe digital video to determine one or more keywords associated with eachof the respective video segments.

The thumbnail generation system can additionally identify a segment ofinterest from the digital video from which to generate a representativeimage. In particular, in one or more embodiments, the thumbnailgeneration system compares keywords corresponding to the content sharingplatform with keywords corresponding to respective segments of thedigital video to identify a segment of interest from the digital videoincluding visual content (e.g., one or more video frames) predicted tomore effectively engage users associated with the content sharingplatform than content from other segments of the digital video.

Upon identifying the segment of interest, the thumbnail generationsystem can generate a representative image for the digital video fromthe segment of interest. For example, the thumbnail generation systemcan identify a video frame from the segment of interest to use as therepresentative image. As another example, the thumbnail generationsystem can generate a thumbnail image having a lower resolution than theidentified frame and utilize the thumbnail image as the representativeimage for the digital video. In either example, the thumbnail generationsystem can provide the representative image via a display of the contentsharing platform.

The thumbnail generation system provides a number of advantages overconventional systems. For example, by identifying a segment of interestassociated with keywords that correspond to digital content items (e.g.,shared images, videos, comments, posts) having a history of engagingusers of the content sharing platform, the thumbnail generation systemcan generate a representative image predicted to effectively engageusers of the content sharing platform. Indeed, rather than generating arandom image from the digital video or simply identifying anyhigh-quality image at an arbitrary point of the digital video to use asa representative image, the thumbnail generation system can moreeffectively engage users of the content sharing platform, resulting inhigher click rates and interaction with digital videos shared via thecommunication system.

In addition, by comparing keywords from the respective segments withkeywords particular to a content sharing platform, the thumbnailgeneration system can generate a representative image unique to anynumber of content sharing platforms. For example, rather thanidentifying a generic thumbnail image to use as a representative imagefor the digital video across multiple content sharing platforms, thethumbnail generation system can generate different representative imagesfor different content sharing platforms predicted to effectively engagedifferent groups of users that access the respective content sharingplatforms. Accordingly, the thumbnail generation system can moreflexibly target different demographics of users of the communicationsystem associated with respective content sharing platforms.

Moreover, by comparing keywords between the content sharing platform andsegments of the digital video to selectively identify a segment ofinterest, the thumbnail generation system significantly reducesconsumption of processing power over conventional systems. Indeed, byisolating a segment of interest and generating the representative imagefrom a selected segment of interest rather than analyzing content overan entire duration of the digital video, the thumbnail generation systemcan identify a representative image without analyzing content over theentire duration of the digital video when generating the representativeimage. Accordingly, the thumbnail generation system can analyzesignificantly fewer video frames of the digital video in generating therepresentative image which significantly reduces computational expenseover conventional systems, thereby resulting in a significantimprovement to the computer system on which the thumbnail generationsystem (e.g., communication system server) is implemented.

As illustrated in the foregoing discussion, the present disclosureutilizes a variety of terms to described features and advantages of thethumbnail generation system. Additional detail is now provided regardingthe meaning of such terms. For instance, as used herein, a “digitalvideo” refers to digital data representative of a sequence of visualimages. By way of example, a digital video may refer to a digital filehaving one of the following file extensions: AVI, FLV, WMV, MOV, MP4.Thus, a digital video includes digital data or a digital file for avideo that is displayable via a graphical user interface of a display ofa computing device. Thus, a digital video includes digital data or adigital file for a video that is displayable via a graphical userinterface of a display of a computing device. A digital video may have acorresponding frame rate, resolution, or duration. In addition, adigital video may include data or information about the digital videofile (e.g., metadata). Moreover, in addition to visual content, adigital video may include additional types of content (e.g., audiocontent) that a viewer experiences when viewing a presentation of thedigital video.

In addition, a digital video may include any number of segments. As usedherein, a “segment” or “video segment” refers to a discrete portion orclip of consecutive content from a digital video. For example, a segmentof a digital video may refer to a clip of the digital video includingany number of consecutive video frames (e.g., depending on a frame rateof the digital video). In one or more embodiments, a plurality ofsegments of a digital video refers to non-overlapping clips of thedigital video. Alternatively, one or more of the plurality of segmentsmay partially overlap. As will be described in further detail below, thethumbnail generation system can divide a digital video into any numberof segments.

As mentioned above, the thumbnail generation system can provide adigital video to users of a communication system via a content sharingplatform of the communication system. As used herein, a “communicationsystem” may refer to one of a variety of different communication systemsincluding, by way of example, a social media system, an electronicmessaging system, a collection of associated webpages, or othernetworking platform over which users of the communication system canshare and access digital content (e.g., digital content items) withother users of the communication system. In addition, as used herein, a“user” of the communication system can refer to any user or other entityhaving access to digital content shared via the communication system. Auser may also refer to any entity having the capability to share digitalcontent with other users of the communication system.

As used herein, a “digital content item” or “digital content” refers toan electronic item. In particular, a digital content item includes anelectronic item provided for display within a content sharing platformof the communication system. Examples of digital content items includedigital images, digital videos, digital text, an electronic document, adigital link, an original or shared post to a content sharing platform,or other type of digital content provided to users of the communicationsystem having access to a content sharing platform.

As mentioned above, users of the communication system can share and/oraccess digital content items provided via a content sharing platform ofthe communication system. As used herein, a “content sharing platform”refers to a page (e.g., a profile page, group page), room (e.g., forum,chatroom), or other virtual space associated with the communicationsystem over which one or more users of the communication system canshare and access digital content items with other users of thecommunication system. By way of example, in one or more embodimentsdescribed herein, a content sharing platform refers to a page accessibleto users generally or a subset of users of the communication systemhosted or otherwise provided by the communication system. Nonetheless,while one or more embodiments described herein relate specifically to acontent sharing platform which includes a group page or profile page ofa social network, features and functionality described in connectionwith the group page can similarly apply to any type of content sharingplatform over which users of a communication system can share and accessdigital content items.

As will be described in further detail herein, the thumbnail generationsystem can generate a representative image for a digital video. As usedherein, a “representative image” refers to a digital imagerepresentative of a digital video provided to users of the communicationsystem via a content sharing platform. For example, a representativeimage may refer to a thumbnail image that includes a single video frameor series of multiple video frames (e.g. an animated GIF) of a digitalvideo that provides a visual preview of the digital video to one or moreusers of the communication system. In one or more embodiments, therepresentative image may include a selectable image that enables a userof a client device to select the image and, in response, access thedigital video represented by the representative image via the contentsharing platform. As will be described in further detail below, thethumbnail generation system can generate and provide a representativeimage for display via a content sharing platform (e.g., via a graphicaluser interface of a client device displaying at least a portion of thecontent sharing platform).

As will be described in further detail below, the thumbnail generationsystem can identify keywords associated with the content sharingplatform and respective segments of a digital video. As used herein, a“keyword” refers to one or more terms associated with a respectivedigital content item (e.g., an image, video, post, comment, document,etc.). In addition, a keyword may refer to one or more terms associatedwith an individual segment of a digital video. As will be described infurther detail below, a keyword may be represented by a keyword vectorwithin a d-dimensional vector space.

In one or more embodiments described herein, the thumbnail generationsystem determines a semantic relationship or semantic correlationbetween identified keywords. As used herein, a “semantic relationship”or “semantic correlation” between two keywords refers to a similaritymetric between the two keywords. For example, in one or more examplesdescribed herein, a semantic relationship between keywords refers to adistance between points on a vector plane where the points refer tovector representations of the keywords. As an example, in one or moreembodiments, a semantic relationship between a first keyword and asecond keyword refers to a Euclidean distance between a first point on avector plane representative of a vector representation of the firstkeyword and a second point on the vector plane representative of avector representation of the second keyword.

Additional detail will now be provided regarding the thumbnailgeneration system in relation to illustrative figures portraying exampleembodiments. For example, FIG. 1 illustrates an example environment 100for generating representative images for digital videos provided via acontent sharing platform of a communication system. As shown in FIG. 1,the environment 100 includes a server device(s) 102 including a campaignmanagement system 104 and a thumbnail generation system 106. Theenvironment 100 further includes a client device 108. As shown in FIG.1, the environment further includes content server device(s) 110including a communication system 112 implemented thereon.

Each of the client device 108 and content server device(s) 110 maycommunicate with the server device(s) 102 by way of the network 114, asshown. The network 114 may include one or multiple networks and may useone or more communication platforms or technologies suitable fortransmitting data. For example, in one or more embodiments, the contentserver device(s) 110 may communicate with the server device(s) 102 byway of a first network while the client device 108 communicates with thecontent server device(s) 110 and/or server device(s) 102 by way of asecond network. In one or more embodiments, the network 114 includes theInternet or World Wide Web. In addition, or as an alternative, thenetwork 114 can include other types of communication networks asdescribed below (e.g., in relation to FIG. 8).

Moreover, the client device 108 may refer to various types of computingdevices. For example, the client device 108 may include a mobile devicesuch as a mobile telephone, a smartphone, a PDA, a tablet, or a laptop.Additionally, or alternatively, the client device 108 may include anon-mobile device such as a desktop computer. In addition, as mentionedabove, the environment 100 includes the server device(s) 102 and contentserver device(s) 110, which may generate, store, receive, and/ortransmit any type of data over the network 114. In addition, asmentioned above, the environment 100 includes the server device(s) 102and the content server device(s) 110. The server device(s) 102, 110 cangenerate, store, receive, and/or transmit any type of data, includingdigital content items, including digital videos to the client device108. Additional detail regarding client devices and server devices isprovided below (e.g., in relation to FIG. 8).

As mentioned above, and as shown in FIG. 1, the server device(s) 102 caninclude the campaign management system 104 and the thumbnail generationsystem 106. The campaign management system 104 can manage, operate, run,and/or execute a digital content campaign. For example, the digitalcampaign management system 104 can receive digital content from apublisher or advertiser, receive or generate campaign parameters (e.g.,a budget, campaign duration, or content selection policies), and thenfacilitate distribution of digital content to one or more clientdevices. For example, in one or more embodiments, the campaignmanagement system 104 determines one or more digital videos to provideto a particular client device or group of client devices. For instance,the campaign management system 104 can identify a digital video fordistribution and provide the digital video to the communication system112 for distribution via a content sharing platform associated with aparticular group of users. The campaign management system 104 canidentify the digital video based on any number of criteria including,for example, demographics of a target audience, or informationassociated with a content sharing platform or users of the contentsharing platform.

As further shown in FIG. 1, the campaign management system 104 includesa thumbnail generation system 106 which determines (e.g., selects,generates) a representative image to use when providing a digital videoto users of the communication system 112 via a particular contentsharing platform. Indeed, as mentioned above, the thumbnail generationsystem 106 can identify and compare a first set of keywords associatedwith a content sharing platform to a second set of keywords associatedwith segments of a digital video to generate a representative image toprovide for display when providing access to the digital video to usersof the content sharing platform. Additional detail with regard toidentifying and comparing keywords between the content sharing platformand segments of the digital video will be described in further detailbelow.

As shown in FIG. 1, the campaign management system 104 and the thumbnailgeneration system 106 are implemented on the server device(s) 102.Nevertheless, in one or more embodiments, the campaign management system104 and/or thumbnail generation system 106 are implemented in whole (orin part) on one or a combination of the client device 108 and/or contentserver device(s) 110. As an example, in one or more embodiments, thecommunication system 112 receives a digital video (e.g., from the serverdevice(s) 102 or a client device of any user of the communication system112) to provide to users of a content management platform hosted by thecommunication system 112 and utilizes the thumbnail generation system106 implemented thereon to generate a representative image to use inproviding a selectable preview via a graphical user interface of theclient device 108 that enables a user of the client device 108 to accessthe digital video.

FIG. 2 illustrates an example framework for generating a representativeimage for a digital video provided to users of a content sharingplatform of the communication system 112. In particular, FIG. 2illustrates an example thumbnail generation model 200 including a videocontent classifier 204, a platform engagement classifier 208, and acontent correlation model 210 in accordance with one or more embodimentsdescribed herein. In particular, FIG. 2 illustrates an example in whichthe thumbnail generation system 106 utilizes the framework of thethumbnail generation model 200 to generate a representative image for adigital video 202 to provide via a graphical user interface of a clientdevice 108 associated with a user that follows, subscribes to, orotherwise has access to a content sharing platform 206 of thecommunication system 112.

In the example shown in FIG. 2 (and in other examples described below),the digital video 202 may refer to any digital video provided to usersof the communication system 112 via a content sharing platform 206 ofthe communication system 112. For example, the digital video 202 mayrefer to a digital advertisement (e.g., a digital video ad) provided viaa newsfeed, user profile, group page profile, or other content sharingplatform hosted by or otherwise associated with the communication system112. As another example, the digital video 202 may refer to any digitalvideo shared by a user of the communication system 112 with other usersof the communication network 112 via a newsfeed, user profile, grouppage profile, or other content sharing platform associated with thecommunication system 112.

As shown in FIG. 2, the thumbnail generation system 106 can utilize thevideo content classifier 204 to determine a plurality of keywordsassociated with segments of the digital video 202. For example, as shownin FIG. 2, the thumbnail generation system 106 divides or otherwiseidentifies segments of the digital video 202 and identifies one or morekeywords associated with each respective segment of the digital video202. As will be described in further detail below, the thumbnailgeneration system 106 can analyze content of each segment of the digitalvideo 202 including visual content, audio content, and displayed textualcontent to determine any number of keywords associated with each segmentof the digital video 202. In one or more embodiments, the thumbnailgeneration system 106 compiles or otherwise generates a record ofsegments and corresponding keywords representative of the segments ofthe digital video.

As further shown in FIG. 2, the thumbnail generation system 106 canutilize the platform engagement classifier 208 to determine one or morekeywords associated with the content sharing platform 206 of thecommunication system 112. For example, as shown in FIG. 2, the thumbnailgeneration system 106 can analyze content shared via the content sharingplatform 206 to identify any number of keywords associated with thecontent sharing platform. As will be described in further detail below,the thumbnail generation system 106 can identify keywords for thecontent sharing platform 206 by analyzing digital content itemspreviously shared to users of the communication system 112 via thecontent sharing platform 206. For example, the thumbnail generationsystem 106 can analyze visual content, audio content, and/or textualcontent of images, videos, posts, comments, and other digital contentitems shared via the content sharing platform.

In addition to generally identifying keywords associated with thecontent sharing platform 206, the thumbnail generation system 106 canadditionally utilize the platform engagement classifier 208 to determinelevels of engagement by users of the communication system 112 withrespect to the identified keywords. For example, as shown in FIG. 2 andas will be described in further detail below, the thumbnail generationsystem 106 can determine an engagement score corresponding to each ofthe identified keywords based on interactions by users of thecommunication system 112 with respect to content shared via the contentsharing platform 206. As used herein, an “interaction” or “userinteraction” with a digital content item refers to tracked interactionsby users of the communication system 112 with regard to the digitalcontent item provided via a content sharing platform of thecommunication system 112. By way of example, an interaction may refer toone or more of a like, comment, share, forward, view, conversion,download or other trackable action by a user of the communication system112 with regard to a digital content item shared via a content sharingplatform.

The thumbnail generation system 106 can additionally train and/orutilize a content correlation model 210 to determine a representativeimage from the digital video 202 to use as a preview image for thedigital video 202 provided to users of the communication system 112 viathe content sharing platform 206. In particular, the thumbnailgeneration system 106 can utilize the content correlation model 210 todetermine correlations between keywords for the individual segments ofthe digital video 202 and keywords for the content sharing platform 206.In particular, the thumbnail generation system 106 can compare keywordsfrom the segments of the digital video 202 with important or engagingkeywords (e.g., keywords associated with threshold engagement scores)associated with the content sharing platform 206 to determinecorrelation scores associated with the segments of the digital video202. The thumbnail generation system 106 can additionally determine asegment of interest by identifying a segment from a plurality ofsegments having the highest correlation score with respect to thekeywords associated with the content sharing platform 206. Additionaldetail with regard to determining the correlation score(s) for thesegments of the digital video 202 will be described below.

Upon identifying the segment of interest from the digital video 202, thethumbnail generation system 106 can perform an act 212 of generating arepresentative image for the digital video 202 to present via a displayof the content sharing platform 206 (e.g., via a graphical userinterface of a client device 108 displaying content of the contentsharing platform 206). For example, the thumbnail generation system 106can identify a video frame of the segment of interest to use as athumbnail image for display within an interface of the content sharingplatform 206. As another example, the thumbnail generation system 106can generate a reduced resolution thumbnail image to use as a previewfor the digital video within an interface of the content sharingplatform 206. In one or more embodiments, the thumbnail generationsystem 106 generates a representative image including multiple videoframes (e.g., a Graphics Interchange Format (GIF) digital file) from thesegment of interest to utilize as a preview for the digital video 202within the content sharing platform 206.

Upon generating the representative image, the thumbnail generationsystem 106 can provide the representative image for display via agraphical user interface of a client device 214 having access to thecontent sharing platform 206. For example, the thumbnail generationsystem 106 can post the digital video 202 to the content sharingplatform 206 by displaying the representative image within apresentation of the content sharing platform 206 (e.g., within agraphical user interface of the client device 214). In one or moreembodiments, in response to detecting a user selection of therepresentative image, the client device 214 can download or otherwiseaccess the digital video 202 and stream, playback, or otherwise presentthe digital video 202 via the graphical user interface of the clientdevice 214.

FIG. 3 illustrates an additional information and details regarding theframework of the thumbnail generation model 200 for generating arepresentative image for a digital video for display via a contentsharing platform of the communication system 112. Indeed, in relation tothe embodiment of FIG. 3, the thumbnail generation model 200 includes avideo content classifier 204 for identifying keywords associated withsegments of the digital video 202. In addition, the thumbnail generationmodel 200 includes a platform engagement classifier 208 for identifyingkeywords and associated engagement scores associated with a contentsharing platform 206. Further, the thumbnail generation model 200includes a content correlation model 210 for identifying a segment ofinterest predicted to effectively engage users of the networking system112 who visit, view, or otherwise access digital content items presentedvia the content sharing platform 206. Indeed, in relation to theembodiment of FIG. 3, components of the prediction model include similarfeatures and functionality described above in connection with FIG. 2, inaddition to additional features and functionality described below.

As mentioned above, the thumbnail generation system 106 can utilize thevideo content classifier 204 to generate or otherwise identify anynumber of keywords associated with respective segments of the digitalvideo 202. In particular, as shown in FIG. 3, in one or moreembodiments, the thumbnail generation system 106 performs an act 302 ofsegmenting the digital video 202. As mentioned above, the thumbnailgeneration system 106 can divide the digital video 202 into any numberof segments or clips.

The thumbnail generation system 106 can divide the digital video 202into segments in a variety of ways. As a first example, in one or moreembodiments, the thumbnail generation system 106 randomly identifiessegments (e.g., of a predetermined length) over a duration of thedigital video 202. For instance, the thumbnail generation system 106 candivide the digital video 202 into a predetermined number of segments or,alternatively, divide the digital video into clips of equal or similarlength that span the duration of the digital video 202. In one or moreembodiments, the thumbnail generation system 106 divides the digitalvideo 202 into a number segments having a particular length where thenumber of segments and length of the segments depends on a duration ofthe digital video 202.

As another example, the thumbnail generation system 106 can detect orotherwise determine breaks in the digital video 202 and divide thedigital video 202 into scenes. For instance, the thumbnail generationsystem 106 can detect non-continuities or cuts between video frames,abrupt changes in visual content over a series of video frames, orsimply extract data from a video file (e.g., scene metadata) to identifyscenes of the digital video 202 and divide the digital video 202 intosegments corresponding to the detected scenes. It will be understoodthat the thumbnail generation system 106 can utilize a variety ofdifferent known methods or algorithms to detect scenes of the digitalvideo 202.

Upon dividing the digital video 202 into any number of segments, thethumbnail generation system 106 can additionally utilize the videocontent classifier 204 to analyze content of the individual segments.For example, as shown in FIG. 3, the thumbnail generation system 106 canperform an act 304 of analyzing visual content, an act 306 of analyzingaudio content, and/or an act 308 of analyzing textual content of thedigital video. In one or more embodiments, the thumbnail generationsystem 106 can analyze visual, audio, and/or textual content for each ofthe plurality of segments. Alternatively, for one or more of theidentified segments, the thumbnail generation system 106 can omit one ormore of the acts 304, 306, 308 of analysis for a corresponding segment(e.g., where no textual or audio content exists for a particularsegment).

The thumbnail generation system 106 can analyze visual content, audiocontent, and/or textual content to identify keywords in a number ofways. For example, with regard to the act 304 of analyzing visualcontent, the thumbnail generation system 106 can utilize a deep learningmodel (e.g., a convolutional neural network) to analyze frames of thedigital video 202 and extract keywords associated with one or a seriesof multiple frames within a corresponding segment. In particular, thethumbnail generation system 106 can utilize a deep learning modeltrained to analyze a sequence of video frames to identify objects orother displayed content within the video frames and extract videometadata for a segment of the digital video 202 including any number ofvideo frames. For example, the thumbnail generation system 106 canutilize the systems and methods described in U.S. patent applicationSer. No. 15/921,492, filed on Mar. 14, 2018, and entitled DetectingObjects Using a Weakly Supervised Model, the entire contents of whichare hereby incorporated by references. In one or more embodiments, thethumbnail generation system 106 determines keywords based on the visualcontent of the digital video 202 by utilizing a cloud-based videointelligence model (e.g., Google Cloud Video Intelligence API).

In addition, with regard to the act 306 of analyzing audio content, thethumbnail generation system 106 can extract keywords based on audio ofeach of the identified segments of the digital video 202. In one or moreembodiments, the thumbnail generation system 106 utilizes a speech totext algorithm to extract one or more keywords corresponding to wordsspoken or otherwise presented via the corresponding segment of thedigital video 202. In one or more embodiments, the thumbnail generationsystem 106 utilizes one or more deep learning models (e.g., deep forwardneural networks, recurrent neural networks) trained to detect andrecognize keywords spoken in one or multiple languages.

Furthermore, with regard to the act 308 of analyzing textual content,the thumbnail generation system 106 can extract keywords based ondetected text displayed within the respective segments of the digitalvideo 202. For example, where one or more video frames of the segmentincludes displayed text, the thumbnail generation system 106 can utilizeoptical character recognition (or other text recognition model) todetect text within the video frame(s) of a segment. The thumbnailgeneration system 106 can additional extract one or more keywordsassociated with the segment from the detected text within the videoframe(s).

Upon identifying any number of terms associated with the segment basedon the visual analysis, audio analysis, and/or textual analysis of thesegment, the thumbnail generation system 106 can additionally perform anact 310 of tagging the segment with one or more keywords from thedetected terms. For example, the thumbnail generation system 106 canconsider results of performing each of the visual analysis, audioanalysis, and textual analysis for a segment to determine one or morekeywords to tag or otherwise associate with the segment of the digitalvideo 202. In particular, in one or more embodiments, the thumbnailgeneration system 106 determines which of the detected terms are mostrelevant and tags the segment of the digital video 202 with the mostrelevant of the terms (e.g., keywords).

In one or more embodiments, the thumbnail generation system 106identifies or otherwise determines confidence values indicative ofrelevance of a particular term with the segment. In particular, theconfidence value may indicate with a high or low level of confidencethat a particular term is indeed associated with a correspondingsegment. For example, where performing one or more of the visualanalysis, audio analysis, and the textual analysis yields a highconfidence value for a particular term (e.g., a confidence value thatexceeds a threshold confidence value), the thumbnail generation system106 can determine that the term is a keyword for the correspondingsegment and tag the segment with the keyword. Alternatively, whereperforming one or more of the analyses yields a low confidence value fora term (e.g., a confidence value below the threshold confidence value),the thumbnail generation system 106 can disregard the term rather thantagging the term as a keyword for the corresponding segment of thedigital video 202. In one or more embodiments, the thumbnail generationsystem 106 identifies a predetermined number of the keywordscorresponding to terms associated with higher confidence values thanother identified terms associated with lower confidence values.

Once keywords are determined for the segments, in one or moreembodiments, the thumbnail generation system 106 generates word vectorsrepresentative of keywords (e.g., keyword vectors) for the correspondingsegments of the digital video. For example, the thumbnail generationsystem 106 can generate a keyword vector in a d-dimensional space (e.g.,d˜300) representative of an identified keyword and tags the segment ofthe digital video 202 with the keyword vector. In accordance with one ormore embodiments described above, the thumbnail generation system 106can tag any number of keyword vectors to metadata of a correspondingsegment of the digital video 202. In one or more embodiments, thethumbnail generation system 106 utilizes the “word2vec” model ingenerating the keyword vectors to represent the identified keywordsassociated with the corresponding segments of the digital video 202. Toillustrate, the thumbnail generation system 106 can utilize the word tovector algorithm, “word2vec” as described in Mikolov, Tomas; Sutskever,Ilya; Chen, Kai; Corrado, Greg S.; Dean, Jeff, Distributedrepresentations of words and phrases and their compositionality, NIPS2013, the entire contents of which are hereby incorporated by reference.

As mentioned above, the thumbnail generation system 106 can additionallyutilize the platform engagement classifier 208 to determine a pluralityof keywords associated with the content sharing platform 206 and furtherdetermine a level of engagement for each of the identified keywords. Asshown in FIG. 3, the thumbnail generation system 106 can perform an act312 of filtering engaging content of the content sharing platform 206.For example, the thumbnail generation system 106 can identify digitalcontent items having a high engagement score (e.g., an engagement scorethat exceeds a threshold engagement score) while disregarding one ormore digital content items having a low engagement score (e.g., anengagement score below a threshold engagement score).

For example, in one or more embodiments, the thumbnail generation system106 iteratively analyzes each of the digital content items shared,posted, or provided via the content sharing platform 206 of thenetworking system 112. In particular, the thumbnail generation system106 can track or otherwise identify interactions by users of thenetworking system 112 with respect to the digital content items. In oneor more embodiments, the thumbnail generation system 106 identifies theinteractions by extracting public data from the content sharing platform206 using a platform specific application programming interface (API)(e.g., a social media page API). In one or more embodiments, the publicdata includes information associated with each shared digital contentitem including, for example, a number of comments, likes, and/or sharesfor the digital content items.

In one or more embodiments, the thumbnail generation system 106generates an engagement score for each digital content items providedvia the content sharing platform 206. For instance, for given digitalcontent item, the thumbnail generation system 106 can determine a scorebased on a number of likes, comments, and shares. As an illustrativeexample, with regard to a post on a social media platform, the thumbnailgeneration system 106 can calculate an engagement score (P) for the postusing the following formula:

P=w ₁(N _(likes))+w ₂(N _(comments))+w ₃(N _(shares))

where N_(likes) refers to a number of “likes” or other user-rating,N_(comments) refers to a number of comments, N_(shares) refers to anumber of shares of the post, and w₁, w₂, and w₃ refer to weightscorresponding to the different types of interactions. The weights mayhave default values of w₁=1, w₂=2, and w₃=3, which can be modified by amarketer, content publisher, or administrative user associated with thecampaign management system 104 (e.g., depending on a particularconversion or engagement goal).

As mentioned above, the thumbnail generation system 106 can filter theengaging content by determining which of the digital content itemsprovided via the content sharing platform 206 satisfy a particularthreshold. For example, the thumbnail generation system 106 can identifyany digital content items having an engagement score that exceeds athreshold engagement score for further analysis (e.g., text extraction)while disregarding any digital content items having an engagement scorebelow the threshold. In this way, the thumbnail generation system 106avoids considering irrelevant or non-engaging digital content items,which limits further analysis to the most relevant digital content itemswhile conserving processing resources of the server device(s) 102(and/or content server device(s) 110).

As mentioned above, the thumbnail generation system 106 can performadditional analysis on those digital content items having higherengagement scores. As shown in FIG. 3, the thumbnail generation system106 utilizes the platform engagement classifier 208 to perform an act314 of extracting one or more keywords from visual content of theidentified digital content items. In particular, the thumbnailgeneration system 106 can analyze visual content of a digital image todetect objects or other content displayed within the digital contentitem and extract keywords associated with the displayed content. In oneor more embodiments, the thumbnail generation system 106 utilizes a deeplearning model (e.g., a convolutional neural network) to detect objectsand/or extract keywords associated with the displayed content of thedigital content item. In one or more embodiments, the thumbnailgeneration system 106 extracts keywords from the visual content usingvarious models including a cloud-based extraction model (e.g., GoogleVision API) or an image tagging model (e.g., Adobe Image Tagging).

The thumbnail generation system 106 can additionally perform an act 316of extracting one or more keywords from textual content of theidentified digital content items. For example, where a digital contentitem refers to a post or comment including textual content, thethumbnail generation system 106 can perform a natural language analysisto parse the textual content and identify one or more keywords from thetextual content of the digital content item. In one or more embodiments,the thumbnail generation system 106 extracts one or more keywords usinga natural language toolkit or other natural language API to extract oneor more keywords from the digital content item.

Similar to the keywords identified in connection with the segments ofthe digital video 202, the thumbnail generation system 106 can similarlyrepresent the keywords extracted from the digital content items providedvia the content sharing platform 206 as vectors. For example, in one ormore embodiments, the thumbnail generation system 106 represents thekeywords for the content sharing platform 206 as keyword vectors in ad-dimensional space (e.g., d˜300) representative of the extracted termsfrom the visual content and/or textual content of the digital contentitems. In one or more embodiments, the thumbnail generation system 106utilizes the “word2vec” model to generate the keyword vectors for thekeywords extracted from the digital content item(s).

As shown in FIG. 3, the thumbnail generation system 106 can additionallyperform an act 318 of determining engagement scores for a plurality ofkeywords extracted from the digital content items (e.g., the digitalcontent items provided via the content sharing platform 206 identifiedas engaging). In one or more embodiments, the thumbnail generationsystem 106 generates record of keywords and corresponding engagementscores by compiling a list of keywords associated with digital contentitems of the content sharing platform 206 and associating engagementscores for the digital content items with the list of keywords. Forexample, where the thumbnail generation system 106 extracts a keyword of“nature” from a digital content item and determines an engagement score(P) for the digital content item to be 0.50, the thumbnail generationsystem 106 can generate a record of keywords including “animal” and acorresponding engagement score (e.g., an integer, percentage, or othernumerical value). As another example, where the thumbnail generationsystem 106 identifies multiple keywords from the digital content item,the thumbnail generation system 106 can generate a record including eachof the keywords and associate the same engagement score to each of theidentifies keywords associated with the corresponding digital contentitem.

In one or more embodiments, the thumbnail generation system 106iteratively generates and updates entries to the record of keywords forthe content sharing platform 206. For example, the thumbnail generationsystem 106 can iteratively analyze each digital content item of thecontent sharing platform 206 to identify one or more keywords andcorresponding engagement score(s) and either add or update the record ofkeywords to reflect the identified keywords and engagement scores. Forinstance, where an identified keyword is not present in the record ofkeywords, the thumbnail generation system 106 can add the identifiedkeyword to the record with the engagement score associated with thecorresponding digital content item.

Alternatively, where an identified keyword for a digital content item isalready present in the record of keywords (e.g., the keyword andcorresponding engagement score was previously identified in connectionwith another digital content item), the thumbnail generation system 106can update the engagement score for the keyword already present in therecord of keywords by incrementing the engagement score from the recordof keywords with the engagement score for the digital content item. Forexample, the thumbnail generation system 106 can increase or decrease avalue of the engagement score for the keyword based on a level ofengagement with the digital content item. In addition, the thumbnailgeneration system 106 can periodically update the record of keywords asadditional digital content items are added to the content sharingplatform and as users of the networking system interact with digitalcontent items of the content sharing platform 206.

Upon extracting or otherwise determining keywords for the segments ofthe digital video 204 and the content sharing platform 206, thethumbnail generation system 106 can additionally utilize the contentcorrelation model 210 to identify a segment of interest of the digitalvideo 202 predicted to more effectively engage users of the networkingsystem 112 that view, interact with, or otherwise access the contentsharing platform 206. For example, as shown in FIG. 3, the thumbnailgeneration system 106 can perform an act 320 of determining segmentcorrelation scores between the segments of the digital video 204 and thecontent sharing platform 206. In particular, the thumbnail generationsystem 106 can determine correlation scores between a first set of oneor more keywords of segments of the digital video 202 identified usingthe video content classifier 204 and a second set of one or morekeywords for the content sharing platform 206 identified using theplatform engagement classifier 208.

In one or more embodiments, the thumbnail generation system 106determines a correlation score for each of the plurality of segments ofthe digital video 202. In particular, the thumbnail generation system106 can determine a correlation score between one or more keywords of asegment and one or more keywords of a content sharing platform based ona semantic relationship (e.g., a semantic correlation) between thekeywords and a level of engagement (e.g., an engagement score) withdigital content items associated with the keywords of the contentsharing platform 206. In particular, in one or more embodiments, thethumbnail generation system 106 can determine a correlation score foreach of the segments of the digital video 202 based on a ratio of asemantic relationship between sets of keywords and one or moreengagement scores associated with the set of keywords associated withthe content sharing platform 206.

The thumbnail generation system 106 can determine a correlation score ina variety of ways. As an example, the thumbnail generation system 106can identify important keywords (e.g., important keywords K_(p)) from aset of keywords associated with the content sharing platform 206. Inparticular, the thumbnail generation system 106 can identify one or morekeywords (e.g., a subset of the record of keywords) from the pluralityof keywords having higher engagement scores than other keywords from theplurality of keywords associated with the content sharing platform 206.In one or more embodiments, the thumbnail generation system 106identifies a predetermined number of the keywords having the highestengagement scores from the set of keywords representative of the contentsharing platform 206 (e.g., the ten most engaging keywords from therecord of keywords).

As noted above, the keywords associated with the content sharingplatform 206 may be represented by word vectors in a d-dimensional space(e.g., d—300). In one or more embodiments, the thumbnail generationsystem 106 generates the keyword vectors for the identified importantkeywords (e.g., where the keyword vectors were not previously generatedfor other identified keywords associated with digital content items ofthe content sharing platform 206). In addition, as will be discussed infurther detail below, a distance between any two points on thed-dimensional space (e.g., corresponding to the vector representationsfor the keywords) represents a semantic relationship between two wordsrepresented by the two points.

To determine a correlation score for a segment of the digital video 202,the thumbnail generation system 106 can determine a semanticrelationship between segment keywords K_(c) (e.g., keywords of thesegment previously identified using the video content classifier 204)and the important keywords K_(p) of the content sharing platform 206. Inone or more embodiments, the thumbnail generation system 106 determinesthe semantic relationship by calculating a distance on the d-dimensionalvector space between one or more of the segment keywords K_(c) and oneor more of the important keywords K_(p) of the content sharing platform206. For example, the thumbnail generation system 106 can calculate aEuclidean distance (d(x, y)) between a first word vector (x) (e.g.,referring to a keyword vector from the important keywords K_(p) of thecontent sharing platform 206) and a second word vector (y) (e.g.,referring to a keyword vector from the segment keywords K_(r)).

In determining the correlation score for the segment, the thumbnailgeneration system 106 determines a correlation between each of thekeywords from the important keywords K_(p) with one or more of thesegment keywords K_(c). To illustrate, in one or more embodiments, thethumbnail generation system 106 identifies a first segment keyword fromthe segment keywords K_(c) having the smallest Euclidean distance (d(x,y)) relative to the other segment keywords K_(c) (e.g., a minimumEuclidean distance (d′(x, y))). Using this minimum Euclidean distance(d′(x, y)), the thumbnail generation system 106 determines a correlationfor the first keyword based on a ratio of the minimum Euclidean distance(d′(x, y)) and the engagement score corresponding to the first keyword(e.g., from the record of keywords for the content sharing platform206). In one or more embodiments, the thumbnail generation system 106determines the correlation for the first keyword using the followingformula:

${{Correlation}\mspace{14mu} {of}\mspace{14mu} {first}\mspace{14mu} {keyword}} = \frac{d^{\prime}( {x,y} )}{P_{x}}$

where d′(x, y) refers to a Euclidean distance between the first keywordof the important keywords K_(p) and an identified keyword from thesegment keywords K_(c) having the closest semantic relationship to thefirst keyword (e.g., an identified keyword from the segment keywordsK_(c) having the closest position in the d-dimensional space to thefirst keyword). In addition, P_(x) refers to an engagement scorecorresponding to the first keyword (e.g., an engagement scorecorresponding to the first keyword from the record of keywords for thecontent sharing platform 206).

As mentioned above, the thumbnail generation system 106 can determine acorrelation between each of the keywords of the important keywords K_(p)and a corresponding keyword from the segment keywords K_(c). Inparticular, the thumbnail generation system 106 can determine acorrelation between each of the important keywords K_(p) and acorresponding keyword from the segment keywords K_(c) determined to havea minimum Euclidean distance (d′(x, y)) from a corresponding keyword ofthe important keywords K_(p). The thumbnail generation system 106 canadditionally determine a correlation score based on the determinedcorrelations for each of the important keywords K_(p). For example, inone or more embodiments, the thumbnail generation system 106 determinesa correlation score for the segment by summing the calculatedcorrelations for each of the important keywords K_(p).

As an alternative, in one or more embodiments, the thumbnail generationsystem 106 determines the correlation score for a segment of the digitalvideo 202 by iteratively updating a correlation score upon determiningcorrelations for each of the important keywords K_(m) For example, inone or more embodiments, the thumbnail generation system 106 initializesa correlation score (S) to zero. Upon determining a correlation for akeyword of the important keywords K_(p), the thumbnail generation system106 can generate an updated correlation score (S′) by adding thecorrelation for the keyword to a current iteration of the correlationscore. Accordingly, referring to the example above in which thethumbnail generation system 106 determines the correlation for the firstkeyword, the thumbnail generation system 106 can determine thecorrelation score (S) using the following equation:

$S^{\prime} = {S + \frac{d^{\prime}( {x,y} )}{P_{x}}}$

where S′ refers to an updated correlation score in view of thedetermined correlation for the first keyword and S refers to a previousiteration of the correlation score prior to determining the correlationfor the first keyword (initialized as S=0). In one or more embodiments,the thumbnail generation system 106 utilizes a similar process todetermine a correlation score for each of the segments of the digitalvideo 202.

As illustrated in FIG. 3, the thumbnail generation system 106 canadditionally perform an act 322 of identifying a segment of interest. Inparticular, upon determining a correlation score for each of thesegments of the digital video 202, the thumbnail generation system 106can identify a segment of interest corresponding to a segment of thedigital video 202 from which to generate a representative imagepredicted to effectively engage users of the networking system 112 viathe content sharing platform 206. In one or more embodiments, thethumbnail generation system 106 identifies the segment of interest byidentifying which segment of the digital video 202 has the lowestcorrelation score (e.g., indicating a high correlation between thesegment and keywords associated with a high level of engagement on thecontent sharing platform 206).

FIG. 4 illustrates an example process for identifying a segment ofinterest in accordance with one or more embodiments described herein.For example, as shown in FIG. 4, the thumbnail generation system 106 canidentify a digital video 402 including segments 404 a-c. Similar to oneor more embodiments described above, the thumbnail generation system 106can divide the digital video 402 into the segments 404 a-c byidentifying scenes of the digital video 402. Alternatively, thethumbnail generation system 106 can divide the digital video 402 intothe segments 404 a-c randomly, uniformly, or using other method(s) fordividing the digital video 402 into discrete clips.

As further shown, the thumbnail generation system 106 can analyze eachof the segments 404 a-c to determine segment keywords 406 a-c associatedwith the respective segments 404 a-c. In addition, the thumbnailgeneration system 106 can determine confidence values 408 a-ccorresponding to the segment keywords 406 a-c. For example, thethumbnail generation system 106 can identify first segment keywords 406a including “car,” “wheel,” and “tire” and corresponding confidencevalues 408 a, second segment keywords 406 b including “nature” and“sunlight” and corresponding confidence values 408 b, and third segmentkeywords 406 c including “squirrel,” “animal,” “wildlife,” and “rodent”and corresponding confidence values 408 c. As indicated above, thethumbnail generation system 106 can determine the segment keywords 406a-c and corresponding confidence values 408 a-c by performing a visualanalysis, audio analysis, and/or textual analysis of content from therespective segments 404 a-c of the digital video 402.

As further illustrated in FIG. 4, the thumbnail generation system 106can analyze digital content items provided via the content sharingplatform 410 to generate a record of keywords including keywords 412associated with the content sharing platform 410 and correspondingengagement scores 414. As described above, the thumbnail generationsystem 106 can determine the keywords 412 associated with the contentsharing platform 410 by extracting the keywords 412 from digital contentitems provided via the content sharing platform 410. In addition, thethumbnail generation system 106 can determine engagement scores 414 forthe extracted keywords based on a number of interactions (e.g., likes,comments, shares) by users of the networking system 112 with respect todigital content items corresponding to the extracted keywords 412.

In addition, the thumbnail generation system 106 can determinecorrelation scores 416 a-c for each of the segments 404 a-c based on acomparison of the keywords 406 a-c associated with the segments 404 a-cand keywords 412 (and engagement scores 414) associated with the contentsharing platform 414. For example, as discussed above, the thumbnailgeneration system 106 can determine a semantic relationship between eachof the keywords 412 (or an identified subset of the set of keywords 412determined to have higher importance than other keywords from the set ofkeywords 412) with each of the keywords 406 a-c for the correspondingsegments 404 a-c of the digital video. In addition, the thumbnailgeneration system 106 can select a segment of interest by determiningwhich of the segments 404 a-c have a correlation score indicating a highcorrelation between the keywords associated with the segment of interestand the keywords 412 associated with the content sharing platform 410).

Upon identifying the segment of interest, the thumbnail generationsystem 106 can generate a representative image for the digital video 402by identifying a video frame to use as a preview image when presentingthe digital video 402 via the content sharing platform 410. For example,where the thumbnail generation system 106 determines that the thirdsegment 404 c has a lower correlation score than the first segment 404 aor the second segment 404 b, the thumbnail generation system 106 candisregard any frames of the first segment 404 a and the second segment404 b and limit analysis to the third segment 404 c to identify a videoframe to use in generating a representative image for the digital video402. Indeed, as mentioned above, by disregarding the first segment 404 aand second segment 404 b to focus additional analysis on the thirdsegment 404 c, the thumbnail generation system 106 can significantlyreduce expense of processing resources when generating therepresentative image for the digital video 402.

By determining a segment of interest based on a correlation betweenkeywords of the segments and a content sharing platform, the thumbnailgeneration system 106 can determine a different segment of interest touse when generating a representative image for different content sharingplatforms. For instance, FIG. 5 illustrates an example embodiment inwhich the thumbnail generation system 106 identifies different segmentsof a digital video to use in generating different representative imagesfor two different content sharing platforms. In particular, as shown inFIG. 5, the thumbnail generation system 106 receives or otherwiseidentifies a digital video 502 including segments 504 a-c to provide tousers of the communication system 112 via a first content sharingplatform 506 and a second content sharing platform 508.

In particular, as shown in FIG. 5, the thumbnail generation system 106can determine correlation scores between a plurality of terms associatedwith the first content sharing platform 506 and keywords associated withrespective segments 504 a-c of the digital video 502 to determine asegment of interest from the plurality of segments 504 a-c. Inparticular, in accordance with one or more embodiments described above,the thumbnail generation system 106 can perform an act 510 ofidentifying the first segment 504 a of the digital video 502 based ondetermining (e.g., based on the correlation score(s)) that keywordsassociated with the first segment 504 a have a stronger correlation toengaging keywords of the first content sharing platform 506 relative tokeywords associated with the second segment 504 b and third segment 504c of the digital video 502.

The thumbnail generation system 106 can utilize a similar process todetermine a different segment of interest from the plurality of segments504 a-c for a second content sharing platform 508. In particular, inaccordance with one or more embodiments described above, the thumbnailgeneration system 106 can perform an act 514 of identifying the secondsegment 504 b of the digital video 502 based on determining (e.g., basedon the correlation score(s)) that keywords associated with the secondsegment 504 b have a stronger correlation to engaging keywords of thesecond content sharing platform 508 relative to keywords associated withthe first segment 504 a and the third segment 504 c of the digital video502.

In providing the digital video 502 to users of the networking system 112via the respective content sharing platforms 506, 508, the thumbnailgeneration system 106 can generate different representative images toprovide as a selectable preview for the digital video 502. Inparticular, as shown in FIG. 5, the thumbnail generation system 106 canprovide a first thumbnail image 512 including an identified video frame(e.g., a reduced resolution image of the video frame) from the firstsegment 504 a to display via a graphical user interface of a clientdevice accessing the first content sharing platform 506. As furthershown, the thumbnail generation system 106 can provide a secondthumbnail image 516 including an identified video frame from the secondsegment 504 b to display via a graphical user interface of a clientdevice accessing the second content sharing platform 508.

As indicated by the foregoing discussion, the thumbnail generationsystem 106 can identify a segment of interest for a digital video fromwhich to generate a representative image for the digital video toprovide via a display of a content sharing platform. Accordingly, theforegoing acts and algorithms described in relations to FIGS. 2-3 cancomprise corresponding structure for a step for determining arepresentative image of the digital video for display via the contentsharing platform based on a semantic relationship between a firstplurality of keywords associated with digital content items provided viathe content sharing platform and a second plurality of keywordsassociated with a plurality of segments of the digital video. As anexample, the thumbnail generation system 106 can identify a segment ofinterest by utilizing the video content classifier 204 to identifykeywords associated with segments of the digital video, utilizing theplatform engagement classifier 208 to identify keywords associated witha content sharing platform, and utilizing the correlation model 210 todetermine a segment of interest from the digital video and generate arepresentative image from the segment of interest predicted to engageusers of the content sharing platform.

Turning now to FIG. 6, additional detail will be provided regardingcomponents and capabilities of an example architecture of the thumbnailgeneration system 106. As mentioned above, the thumbnail generationsystem 106 can be implemented by a variety of computing devicesincluding server device(s) 102, the content server device(s) 110, theclient device 108, or a combination of multiple devices. In particular,FIG. 6 illustrates one implementation of the thumbnail generation system106 implemented within a campaign management system 104 on the serverdevice(s) 102 and having similar features and functionality associatedwith one or more embodiments described above. For example, the thumbnailgeneration system 106 can provide features and functionality forgenerating a representative image (e.g., a thumbnail image) for adigital video and providing the representative image as a preview of thedigital video within a display of the content sharing platform on aclient device.

As shown in FIG. 6, the thumbnail generation system 106 includes a videotagging manager 602, which includes a segment identifier 604 and asegment keyword extractor 606. The thumbnail generation system 106additionally includes a platform engagement manager 608, which includesa platform keyword extractor 610 and a keyword engagement manager 612.As further shown in FIG. 6, the thumbnail generation system 106 includesa correlation manager 614, a representative image generator 616, anddata storage 618, which includes video data 620 and platform data 622.

In one or more embodiments, each of the components of the thumbnailgeneration system 106 are in communication with one another using anysuitable communication technologies. Additionally, the components of thethumbnail generation system 106 can be in communication with one or moreother devices including the client device 108, server device(s) 102, andcontent server device(s) 110, as illustrated in FIG. 1. It will berecognized that although the components of the thumbnail generationsystem 106 are shown to be separate in FIG. 6, any of the subcomponentsmay be combined into fewer components, such as into a single component,or divided into more components as may serve a particularimplementation. Furthermore, although the components of FIG. 6 aredescribed in connection with the thumbnail generation system 106, atleast some of the components for performing operations in conjunctionwith the thumbnail generation system 106 described herein may beimplemented on other devices within the environment (e.g., environment100).

The components of the thumbnail generation system 106 can includesoftware, hardware, or both. For example, the components of thethumbnail generation system 106 can include one or more instructionsstored on a computer-readable storage medium and executable byprocessors of one or more computing devices (e.g., the serverdevice(s)). When executed by the one or more processors, thecomputer-executable instructions of the thumbnail generation system 106can cause the server device(s) 102 to perform the methods describedherein. Alternatively, the components of the thumbnail generation system106 can comprise hardware, such as a special purpose processing deviceto perform a certain function or group of functions. Additionally oralternatively, the components of the thumbnail generation system 106 caninclude a combination of computer-executable instructions and hardware.

Furthermore, the components of the thumbnail generation system 106performing the functions described herein with respect to the thumbnailgeneration system 106 may, for example, be implemented as part of astand-alone application, as a module of an application, as a plug-in forapplications including content management applications, as a libraryfunction or functions that may be called by other applications, and/oras a cloud-computing model. Thus, the components of the digital videoselection system 106 may be implemented as part of a stand-aloneapplication on a personal computing device or a mobile device.Alternatively or additionally, the components of the thumbnailgeneration system 106 may be implemented in any application that allowsproduct and customer management, including, but not limited to,applications in ADOBE® ANALYTICS CLOUD, such as ADOBE® ANALYTICS, ADOBE®AUDIENCE MANAGER, ADOBE® CAMPAIGN, ADOBE® EXPERIENCE MANAGER, and ADOBE®TARGET. “ADOBE”, “ADOBE ANALYTICS CLOUD”, “ADOBE ANALYTICS”, “ADOBEAUDIENCE MANAGER”, “ADOBE CAMPAIGN”, “ADOBE EXPERIENCE MANAGER”, and“ADOBE TARGET” are registered trademarks of Adobe Systems Incorporatedin the United States and/or other countries.

As shown in FIG. 6, the thumbnail generation system 106 includes a videotagging manager 602 for identifying keywords associated with segments ofa digital video for sharing via a content sharing platform. For example,upon receiving or otherwise identifying a digital video to share withusers of the communication system 112 via a content sharing platform ofthe communication system 112, the video tagging manager 602 can identifyone or more keywords associated with segments that make up the digitalvideo.

As mentioned above, and as shown in FIG. 6, the video tagging manager602 includes a segment identifier 604. In accordance with one or moreembodiments described above, the segment identifier 604 can identify anynumber of segments of a digital video in a variety of ways. For example,the segment identifier 604 can detect scenes of the digital video anddivide the digital video into segments corresponding to the detectedscenes. Alternatively, in one or more embodiments, the segmentidentifier 604 divides the digital video into random and/or uniformlength segments.

As further shown in FIG. 6, the video tagging manager 602 includes asegment keyword extractor 606. In accordance with one or moreembodiments described above, the segment keyword extractor 606 cananalyze content of the plurality of segments to identify keywordsassociated with respective segments of the digital video. For example,the segment keyword extractor 606 can analyze visual content, audiocontent, and/or textual content of the segments to extract one or morekeywords associated with each of the segments.

As further shown in FIG. 6, the thumbnail generation system 106 includesa platform engagement manager 608 for identifying keywords associatedwith a content sharing platform and associated engagement levels. Forexample, in one or more embodiments, upon receiving a request to sharethe digital video with users of the communication system 112 via thecontent sharing platform, the platform engagement manager 608 candetermine a set of keywords associated with the content sharing platformin accordance with one or more embodiments described herein.

As mentioned above, and as shown in FIG. 6, the platform engagementmanager 608 includes a platform keyword extractor 610. In accordancewith one or more embodiments described above, the platform keywordextractor 610 can analyze digital content items shared via the contentsharing platform to identify keywords associated with the digitalcontent items. For example, the platform keyword extractor 610 cananalyze text of comments, documents, metadata or other digital contentitems having associated text to identify keywords associated with thedigital content items. In addition (or as an alternative), the platformkeyword extractor 610 can analyze visual content of images, videoframes, or other digital content items to extract keywords.

In addition, the platform engagement manager 608 can include a keywordengagement manager 612. In particular, the keyword engagement manager612 can determine an engagement score for the identified keywords. Inone or more embodiments, the keyword engagement manager 612 determinesthe correlation scores by identifying a number of interactions by usersof the communication system 112 with respect to the digital contentitems associated with the extracted keywords. The keyword engagementmanager 612 can further calculate an engagement score based on thenumber of interactions with respect to the relevant digital contentitems.

As shown in FIG. 6, the thumbnail generation system 106 includes acorrelation manager 614. In accordance with one or more embodimentsdescribed above, the correlation manager 614 can determine a correlationscore between a set of keywords associated with the content sharingplatform and one or more keywords associated with each of the respectivesegments of the digital video. In particular, the correlation manager614 can determine correlation scores for the segments of the digitalvideo based on a combination of a semantic relationship between the setof keywords associated with the content sharing platform and the one ormore keywords associated with the respective segments of the digitalvideo and engagement scores associated with the set of keywords. Asdiscussed above, the correlation manager 614 can determine a correlationscore for each of the segments of the digital video.

As further shown in FIG. 6, the thumbnail generation system 106 includesa representative image generator 616. In accordance with one or moreembodiments described above, the representative image generator 616 canidentify a representative image to use as a preview for the digitalvideo within a display of the content sharing platform. In particular,in one or more embodiments, the representative image generator 616identifies a segment of interest corresponding to a highest correlationscore from the plurality of segments. In addition, the representativeimage generator 616 can determine or otherwise generate a representativeimage from the segment of interest. In one or more embodiments, therepresentative image generator 616 can provide (e.g., via a graphicaluser interface of a client device) the representative image as a previewfor the digital video within a display of the content sharing platform.

As illustrated in FIG. 6, the thumbnail generation system 106 includes adata storage 618 including video data 620. The video data 620 caninclude any data from a digital video file corresponding to a digitalvideo. For example, the video data 620 may include visual content, audiocontent, text content (e.g., text displayed within one or more frames ofa digital video), or any data from the metadata of a digital video file.

As further shown, the data storage 618 includes platform data 622. Theplatform data 622 can include any data associated with a content sharingplatform. For example, the platform data 622 includes data associatedwith digital content items shared via the content sharing platformincluding shared images, videos, comments, posts, documents, or otherdigital content items accessible to users of the communication system112 via the content sharing platform. The platform data 622 canadditionally include information about interactions by users of thecommunication system 112 with respect to digital content items sharedvia the content sharing platform.

Turning now to FIG. 7, this figure illustrates a flowchart including aseries of acts 700 for identifying a segment of interest from aplurality of segments of a digital video and generating a representativeimage for the digital video from the identified segment of interest.While FIG. 7 illustrates acts according to one or more embodiments,alternative embodiments may omit, add to, reorder, and/or modify any ofthe acts shown in FIG. 7. The acts of FIG. 7 can be performed as part ofa method. Alternatively, a non-transitory computer readable medium cancomprise instructions, that when executed by one or more processors,cause a computing device to perform the acts of FIG. 7. In still furtherembodiments, a system can perform the acts of FIG. 7.

For example, the series of acts 700 includes an act 710 of determining afirst set of keywords for a content sharing platform of a communicationsystem 112. For instance, in one or more embodiments, the act 710includes determining a first set of keywords for a content sharingplatform of a communication system 112 corresponding to a plurality ofdigital content items shared with users of the communication system 112via the content sharing platform. In one or more embodiments, thecontent sharing platform includes one or more of a webpage, a socialmedia newsfeed, or a group page.

In one or more embodiments, determining the first set of keywordsincludes identifying a plurality of keywords associated with a subset ofdigital content items shared with users of the communication system viathe content sharing platform. The subset of digital content items mayinclude digital content items associated with a higher number ofinteractions by users of the communication system than other digitalcontent items from the plurality of digital content items.

The series of acts 700 further includes an act 720 of identifying adigital video including a plurality of segments associated with a secondset of keywords to share via the content sharing platform. For example,in one or more embodiments, the act 720 includes identifying a digitalvideo to share via the content sharing platform where the digital videoincludes a plurality of segments associated with a second set ofkeywords and the second set of keywords includes one or more keywordscorresponding to each of the plurality of segments. In one or moreembodiments, identifying the digital video to share includes receiving adigital video advertisement to provide to users of the communicationsystem 112 via the content sharing platform. In one or more embodiments,identifying the digital video to share includes receiving, from a clientdevice associated with a user of the communication system 112, a requestto share the digital video with the users of the communication system112 via a post on the content sharing platform.

In one or more embodiments, the series of acts 700 includes dividing thedigital video into the plurality of segments. For example, in one ormore embodiments, the series of acts 700 includes detecting a pluralityof scenes of the digital video. In addition, in one or more embodiments,the series of acts 700 includes dividing the digital video into theplurality of segments based on the detected plurality of scenes of thedigital video.

In one or more embodiments, the series of acts 700 includes extractingone or more keywords from each segment of the plurality of segments. Forexample, in one or more embodiments, the series of acts 700 includesanalyzing visual content of video frames of a segment to determine atleast one keyword associated with the video frames of the segment. Inaddition, in one or more embodiments, the series of acts 700 includesanalyzing audio content of the segment to determine one or moreadditional keywords associated with the audio content of the segment.The series of acts 700 can additionally include analyzing textualcontent of the segment to determine one or more additional keywordsassociated with the textual content of the segment.

The series of acts 700 further includes an act 730 of identifying asegment of interest from the plurality of segments based on acorrelation score between one or more keywords corresponding to thesegment of interest and the first set of keywords for the contentsharing platform. For example, in one or more embodiments, the act 730includes identifying a segment of interest from the plurality ofsegments based on a correlation score between one or more keywordscorresponding to the segment of interest and the first set of keywordsfor the content sharing platform where the correlation score is based ona semantic relationship between the one or more keywords correspondingto the segment of interest and the first set of keywords for the contentsharing platform.

In one or more embodiments, the series of acts 700 includes determiningthe correlation score(s) associated with the plurality of segmentsfurther based on engagement scores reflective of a level of engagementby users of the communication system 112 with respect to the pluralityof digital content items associated with the first set of keywords. Inone or more embodiments, the series of acts 700 includes determiningengagement scores for the first set of keywords. In one or moreembodiments, determining the engagement scores includes, for eachkeyword from the first set of keywords, identifying one or more digitalcontent items from the plurality of digital content items associatedwith a keyword; and determining the engagement score for the keywordbased on a number of interactions by users of the communication system112 with respect to the one or more digital content items.

In one or more embodiments, the series of acts 700 includes determiningsemantic relationships between one or more keywords corresponding toeach of the plurality of segments and the first set of keywordsassociated with the content sharing platform. In addition, in one ormore embodiments, the series of acts 700 includes identifying thesegment of interest by determining that the one or more keywordscorresponding to the segment of interest have a closer semanticrelationship with the first set of keywords than one or more keywordscorresponding to other segments of the plurality of segments.

In one or more embodiments, determining the semantic relationships forthe plurality of segments includes generating a plurality of vectorrepresentations corresponding to the first set of keywords for thecontent sharing platform. In addition, determining the semanticrelationship for the segments may include, for each segment of theplurality of segments, generating one or more vector representations forthe one or more keywords associated with a segment; and determining, foreach vector representation of the plurality of vector representations, adistance on a vector plane from at least one or the one or more vectorrepresentations for the one or more keywords associated with thesegment.

The series of acts 700 further includes an act 740 of determining arepresentative image from the segment of interest. For example, in oneor more embodiments, the act 740 includes determining a representativeimage from the segment of interest. In one or more embodiments,determining the representative image includes generating areduced-resolution image corresponding to a video frame from the segmentof interest.

The series of acts 700 further includes an act 750 of providing therepresentative image within a display of the content sharing platform.For example, in one or more embodiments, the act 750 includes providing,via a graphical user interface of a client device, the representativeimage as a preview for the digital video within a display of the contentsharing platform. In one or more embodiments, the series of acts 700includes detecting a user selection of the representative image via thegraphical user interface on the client device. In addition, in one ormore embodiments, the series of acts 700 includes in response todetecting the user selection of the representative image, providing thedigital video for display on the client device within the display of thecontent sharing platform.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations including personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 8 illustrates a block diagram of exemplary computing device 800that may be configured to perform one or more of the processes describedabove. As shown by FIG. 8, the computing device 800 can comprise aprocessor 802, a memory 804, a storage device 806, an I/O interface 808,and a communication interface 810, which may be communicatively coupledby way of a communication infrastructure 812. In certain embodiments,the computing device 800 can include fewer or more components than thoseshown in FIG. 8. Components of the computing device 800 shown in FIG. 8will now be described in additional detail.

In one or more embodiments, the processor 802 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions fordigitizing real-world objects, the processor 802 may retrieve (or fetch)the instructions from an internal register, an internal cache, thememory 804, or the storage device 806 and decode and execute them. Thememory 804 may be a volatile or non-volatile memory used for storingdata, metadata, and programs for execution by the processor(s). Thestorage device 806 includes storage, such as a hard disk, flash diskdrive, or other digital storage device, for storing data or instructionsrelated to object digitizing processes (e.g., digital scans, digitalmodels).

The I/O interface 808 allows a user to provide input to, receive outputfrom, and otherwise transfer data to and receive data from computingdevice 800. The I/O interface 808 may include a mouse, a keypad or akeyboard, a touch screen, a camera, an optical scanner, networkinterface, modem, other known I/O devices or a combination of such I/Ointerfaces. The I/O interface 808 may include one or more devices forpresenting output to a user, including, but not limited to, a graphicsengine, a display (e.g., a display screen), one or more output drivers(e.g., display drivers), one or more audio speakers, and one or moreaudio drivers. In certain embodiments, the I/O interface 808 isconfigured to provide graphical data to a display for presentation to auser. The graphical data may be representative of one or more graphicaluser interfaces and/or any other graphical content as may serve aparticular implementation.

The communication interface 810 can include hardware, software, or both.In any event, the communication interface 810 can provide one or moreinterfaces for communication (such as, for example, packet-basedcommunication) between the computing device 800 and one or more othercomputing devices or networks. As an example and not by way oflimitation, the communication interface 810 may include a networkinterface controller (NIC) or network adapter for communicating with anEthernet or other wire-based network or a wireless NIC (WNIC) orwireless adapter for communicating with a wireless network, such as aWI-FI.

Additionally, the communication interface 810 may facilitatecommunications with various types of wired or wireless networks. Thecommunication interface 810 may also facilitate communications usingvarious communication protocols. The communication infrastructure 812may also include hardware, software, or both that couples components ofthe computing device 800 to each other. For example, the communicationinterface 810 may use one or more networks and/or protocols to enable aplurality of computing devices connected by a particular infrastructureto communicate with each other to perform one or more aspects of thedigitizing processes described herein. To illustrate, the imagecompression process can allow a plurality of devices (e.g., serverdevices for performing image processing tasks of a large number ofimages) to exchange information using various communication networks andprotocols for exchanging information about a selected workflow and imagedata for a plurality of images.

In the foregoing specification, the present disclosure has beendescribed with reference to specific exemplary embodiments thereof.Various embodiments and aspects of the present disclosure(s) aredescribed with reference to details discussed herein, and theaccompanying drawings illustrate the various embodiments. Thedescription above and drawings are illustrative of the disclosure andare not to be construed as limiting the disclosure. Numerous specificdetails are described to provide a thorough understanding of variousembodiments of the present disclosure.

The present disclosure may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel with one another or inparallel with different instances of the same or similar steps/acts. Thescope of the present application is, therefore, indicated by theappended claims rather than by the foregoing description. All changesthat come within the meaning and range of equivalency of the claims areto be embraced within their scope.

1. A method comprising: identifying, by a server device of acommunication system, a digital video to provide to users of thecommunication system via a content sharing platform of the communicationsystem; a step for determining a representative image of the digitalvideo for display via the content sharing platform based on a semanticrelationship between a first plurality of keywords associated withdigital content items provided via the content sharing platform and asecond plurality of keywords associated with a plurality of segments ofthe digital video, the representative image comprising a thumbnail imagepredicted to engage a user of the communication system via the contentsharing platform; and providing, via a graphical user interface of aclient device associated with the user, the representative image as acustomized preview for the digital video within a display of the contentsharing platform.
 2. The method of claim 1, wherein identifying thedigital video to share comprises receiving a digital video advertisementto provide to users of the communication system via the content sharingplatform.
 3. The method of claim 1, wherein identifying the digitalvideo to share comprises receiving, from a client device associated witha user of the communication system, a request to share the digital videowith the users of the communication system via a post on the contentsharing platform.
 4. The method of claim 1, wherein the content sharingplatform comprises one or more of a webpage, a social media newsfeed, ora group page.
 5. A system comprising: at least one processor; anon-transitory computer readable medium storing instructions thereonthat, when executed by at least one processor, cause the system to:determine a first set of keywords for a content sharing platform of acommunication system corresponding to a plurality of digital contentitems shared with users of the communication system via the contentsharing platform; identify a digital video to share via the contentsharing platform, the digital video comprising a plurality of segmentsassociated with a second set of keywords, the second set of keywordscomprising one or more keywords corresponding to each of the pluralityof segments; identify a segment of interest from the plurality ofsegments based on a correlation score between one or more keywordscorresponding to the segment of interest and the first set of keywordsfor the content sharing platform, wherein the correlation score is basedon a semantic relationship between the one or more keywordscorresponding to the segment of interest and the first set of keywordsfor the content sharing platform; determine a representative image fromthe segment of interest, the representative image comprising a thumbnailimage predicted to engage a user of the communication system via thecontent sharing platform; and provide, via a graphical user interface ofa client device associated with the user, the representative image as acustomized preview for the digital video within a display of the contentsharing platform.
 6. The system of claim 5, wherein the correlationscore is further based on engagement scores reflective of a level ofengagement by users of the communication system with respect to theplurality of digital content items associated with the first set ofkeywords.
 7. The system of claim 6, further comprising instructionsthat, when executed by the at least one processor, cause the system todetermine engagement scores for the first set of keywords, whereindetermining the engagement scores comprises, for each keyword from thefirst set of keywords: identifying one or more digital content itemsfrom the plurality of digital content items associated with a keyword;and determining the engagement score for the keyword based on a numberof interactions by users of the communication system with respect to theone or more digital content items.
 8. The system of claim 5, whereindetermining the first set of keywords comprises identifying a pluralityof keywords associated with a subset of digital content items sharedwith users of the communication system via the content sharing platform,wherein the subset of digital content items comprises digital contentitems associated with a higher number of interactions by users of thecommunication system than other digital content items from the pluralityof digital content items.
 9. The system of claim 5, further comprisinginstructions that, when executed by the at least one processor, causethe system to: determine semantic relationships between one or morekeywords corresponding to each of the plurality of segments and thefirst set of keywords; and identify the segment of interest bydetermining that the one or more keywords corresponding to the segmentof interest have a closer semantic relationship with the first set ofkeywords than one or more keywords corresponding to other segments ofthe plurality of segments.
 10. The system of claim 9, whereindetermining the semantic relationships between one or more keywordscorresponding to each of the plurality of segments and the first set ofkeywords comprises: generating a plurality of vector representationscorresponding to the first set of keywords for the content sharingplatform; and for each segment of the plurality of segments: generatingone or more vector representations for the one or more keywordsassociated with a segment; and determining, for each vectorrepresentation of the plurality of vector representations, a distance ona vector plane from at least one or the one or more vectorrepresentations for the one or more keywords associated with thesegment.
 11. The system of claim 5, further comprising instructionsthat, when executed by the at least one processor, causes the system toextract one or more keywords from each segment of the plurality ofsegments.
 12. The system of claim 11, wherein extracting one or morekeywords from the plurality of segments comprises, for each segment,analyzing visual content of video frames of the segment to determine atleast one keyword associated with the video frames of the segment. 13.The system of claim 12, wherein extracting one or more keywords from theplurality of segments further comprises, for each segment: analyzingaudio content of the segment to determine one or more additionalkeywords associated with the audio content of the segment; or analyzingtextual content of the segment to determine one or more additionalkeywords associated with the textual content of the segment.
 14. Anon-transitory computer-readable storage medium comprising instructionsthereon that, when executed by at least one processor, cause a computersystem to: determine a first set of keywords for a content sharingplatform of a communication system corresponding to a plurality ofdigital content items shared with users of the communication system viathe content sharing platform; identify a digital video to share via thecontent sharing platform, the digital video comprising a plurality ofsegments associated with a second set of keywords, the second set ofkeywords comprising one or more keywords corresponding to each of theplurality of segments; identify a segment of interest from the pluralityof segments based on a correlation score between one or more keywordscorresponding to the segment of interest and the first set of keywordsfor the content sharing platform, wherein the correlation score is basedon a semantic relationship between the one or more keywordscorresponding to the segment of interest and the first set of keywordsfor the content sharing platform; determine a representative image fromthe segment of interest, the representative image comprising a thumbnailimage predicted to engage a user of the communication system via thecontent sharing platform; and provide, via a graphical user interface ofa client device associated with the user, the representative image as acustomized preview for the digital video within a display of the contentsharing platform.
 15. The computer-readable storage medium of claim 14,wherein the correlation score is further based on engagement scoresreflective of a level of engagement by users of the communication systemwith respect to the plurality of digital content items associated withthe first set of keywords.
 16. The computer-readable storage medium ofclaim 14, further comprising instructions that, when executed by the atleast one processor, cause the computer system to determine engagementscores for the first set of keywords, wherein determining the engagementscores comprises, for each keyword from the first set of keywords:identifying one or more digital content items from the plurality ofdigital content items associated with a keyword; and determining theengagement score for the keyword based on a number of interactions byusers of the communication system with respect to the one or moredigital content items.
 17. The computer-readable storage medium of claim14, further comprising instructions that, when executed by the at leastone processor, cause the computer system to divide the digital videointo the plurality of segments by: detecting a plurality of scenes ofthe digital video; and dividing the digital video into the plurality ofsegments based on the detected plurality of scenes of the digital video.18. The computer-readable storage medium of claim 14, whereindetermining the representative image from the segment of interestcomprises generating a reduced-resolution image corresponding to a videoframe from the segment of interest.
 19. The computer-readable storagemedium of claim 14, further comprising instructions that, when executedby the at least one processor, cause the computer system to: detecting auser selection of the representative image via the graphical userinterface on the client device; and in response to detecting the userselection of the representative image, providing the digital video fordisplay on the client device within the display of the content sharingplatform.
 20. The computer-readable storage medium of claim 14, furthercomprising instructions that, when executed by the at least oneprocessor, cause the computer system to extract one or more keywordsfrom each segment of the plurality of segments, wherein extracting oneor more keywords from the plurality of segment comprises, for eachsegment from the plurality of segments: analyzing visual content ofvideo frames of a segment to determine at least one keyword associatedwith the video frames of the segment; analyzing audio content of thesegment to determine one or more additional keywords associated with theaudio content of the segment; and analyzing textual content of thesegment to determine one or more additional keywords associated with thetextual content of the segment.